Fitting consistent knowledge into the planning process: An integrated database on adaptation and mitigation measures in Europe

Climate action is far from meeting the internationally agreed adaptation and mitigation goals. Even though climate action planning has increased since the Paris Agreement in 2015, the implementation rate of those plans remains low. Climate planning literature claims that accounting for long-term planning and implementation times, accurately estimating costs, identifying synergies and trade-offs between measures, or considering justice and equity issues might increase the quality of climate plans and facilitate the further implementation of climate actions. Also, there is no uniform way of responding to the climate crisis. Existing climate action databases typically focus on a particular type of response, sector, hazard, or type. In parallel, national governments and international initiatives provide tools and guidelines to facilitate the development of climate action plans. However, the primary climate action recording and monitoring initiatives and projects do not share the same framework as those tools, resulting in a lost opportunity to improve climate actions' knowledge transferability. Thus, we reviewed nine existing databases of adaptation and five mitigation databases, comprising a total of 7.130 adaptation actions and 11.409 mitigation actions, and detected a lack of alignment with climate planning practices and claims. Furthermore, we revealed a lack of coherency regarding the level of abstraction of climate actions and their role in the implementation process. Not all climate actions are meant to operate similarly from a planning perspective: while some had a direct outcome on the target indicators, others are thought to facilitate their implementation. Ultimately, we created a new integrated database of adaptation and mitigation measures in Europe, focusing exclusively on climate planning and implementation practices. First, we identified specific and transferable mitigation and adaptation measures and instruments through an originally designed decision tree. Second, we harmonised the collection of climate actions in a unique framework based on one of the biggest climate planning initiatives: the Sustainable and Energy Climate Action Plans by the Covenant of Mayors. Our integrated database of adaptation and mitigation measures (1) classifies and relates the different types of climate actions; (2) provides data that may improve the quality of climate plans and facilitate implementation; (3) allows a better perspective of systematic problems by identifying potential synergies and trade-offs; and (4) defines and characterises measures using a framework that draws on actual practice. The database compiles a total of 191 adaptation measures, 188 mitigation measures, and 97 measures that account for each, and a total of 609 associated instruments. For monitoring their outcomes, 93 Sustainable Development Goals relevant indicators are included.


a b s t r a c t
Climate action is far from meeting the internationally agreed adaptation and mitigation goals.Even though climate action planning has increased since the Paris Agreement in 2015, the implementation rate of those plans remains low.Climate planning literature claims that accounting for longterm planning and implementation times, accurately estimating costs, identifying synergies and trade-offs between measures, or considering justice and equity issues might increase the quality of climate plans and facilitate the further implementation of climate actions.Also, there is no uniform way of responding to the climate crisis.Existing climate action databases typically focus on a particular type of response, sector, hazard, or type.In parallel, national governments and international initiatives provide tools and guidelines to facilitate the development of climate action plans.However, the primary climate action recording and monitoring initiatives and projects do not share the same framework as those tools, resulting in a lost opportunity to improve climate actions' knowledge transferability.
Thus, we reviewed nine existing databases of adaptation and five mitigation databases, comprising a total of 7.130 adaptation actions and 11.409 mitigation actions, and detected a lack of alignment with climate planning practices and claims.Furthermore, we revealed a lack of coherency regarding the level of abstraction of climate actions and their role in the implementation process.Not all climate actions are meant to operate similarly from a planning perspective: while some had a direct outcome on the target indicators, others are thought to facilitate their implementation.Ultimately, we created a new integrated database of adaptation and mitigation measures in Europe, focusing exclusively on climate planning and implementation practices.First, we identified specific and transferable mitigation and adaptation measures and instruments through an originally designed decision tree.Second, we harmonised the collection of climate actions in a unique framework based on one of the biggest climate planning initiatives: the Sustainable and Energy Climate Action Plans by the Covenant of Mayors.Our integrated database of adaptation and mitigation measures (1) classifies and relates the different types of climate actions; (2) provides data that may improve the quality of climate plans and facilitate implementation; (3) allows a better perspective of systematic problems by identifying potential synergies and trade-offs; and (4) defines and characterises measures using a framework that draws on actual practice.The database compiles a total of 191 adaptation measures, 188 mitigation measures, and 97 measures that account for each, and a total of 609 associated instruments.For monitoring their outcomes, 93

Value of the Data
• Focus on implementation: climate actions in the database are segregated according to their role in the planning process towards implementation.Measures are climate actions that directly impact their environment, can be implemented, and respond to a mitigation or adaptation goal [ 1 , 2 ] -installing solar panels.On the other hand, instruments facilitate the implementation of measures and do not have an outcome directly related to climate mitigation or adaptation [ 1 , 2 ] -subsidies to households for installing solar panels.

• Coherency of climate actions:
We provide a harmonised dataset of mitigation and adaptation measures at a similar abstraction level.Thus, all are detailed enough to ensure proper transferability to climate plans.Following the previous framework, relevant instruments and indicators are also provided per measure.• Useful data from the planning perspective: The database offers planners not only a set of data, but a structure to follow to boost implementation.An appropriate planning scale [ 3 , 4 ], the proper consideration of implementation times and long-term planning [ 3 , 4 ], and the consideration of justice [ 5,18 ] increase the feasibility of climate actions.Identifying synergies and trade-offs is beneficial for implementing climate action and increasing their effectiveness [ 3 , 4 , 6 ], but they are not typically included in the consulted sources.• Transferable climate planning practices framework: Even if implementation depend on local and regional factors, trusting systematic approaches offers a solution to speed up climate action.The Covenant of Mayors is one of the biggest networks, with more than 11.0 0 0 signatories committed to developing a Sustainable Energy Climate Action Plan (SECAP).Thus, we aligned our data with their SECAPs template, thereby offering climate planners directly transferable data for developing climate plans.
• Integrated approach: The database integrates mitigation and adaptation measures and identifies measures with outcomes in both response types.Moreover, it identifies synergies within sectors and hazards, provides hints about synergies with SDGs through indicators, and attaches instruments to each measure.As the data comes from relevant European initiatives based on on-ground research, it provides a good overview of how the climate response is in Europe.• Replicable methodology: Climate action is highly context-dependent [ 2 , 3 , 4 ].Moreover, new technologies and measures to cope with climate change mitigation and adaptation are being constantly developed.Thus, our work relies not only on data compilation but on a replicable and transparent method, allowing us to easily expand the database with new measures and instruments.

Relational database
The database [ 19 ] is compiled in a single Microsoft Excel file comprising four data sheets which are described in Table 1 .The four datasheets altogether compose a relational database.Thus, each datasheet is related to the other through specific relationships based on the climate planning framework described in Fig. 1 .The code that allows the database to function can be found in Table S2 of the Supplementary Materials.
From a climate planning perspective, climate actions are not equal in function and outcome.Plans are composed of a series of climate actions and their targeted goals.Measures are climate actions that can be implemented and directly impact their environment by targeting a  mitigation or adaptation goal and modifying the correspondent indicator [ 1 , 2 ].More specifically, instruments aim to facilitate the implementation of measures and modify their implementation conditions [ 1 , 2 ].Measures and instruments can sometimes be grouped if they share targets and sectors.Such groups are named options [ 2 , 6 , 8 ].While options might provide a good summary of previous actions, their breadth makes extracting specific actions difficult and burdens transferability from a planning perspective.An example can be found in Table 2 , where in the option Resilient communities to flooding we find several specific measures, like Heighteniing dikes or Floating roads.Then, each measure has a number of attached instruments which can facilitate or improve their implementation, like Collecting high-quality data for flood recovery or develop Sustainable mobility plans in the cities , and a number of SOIs which can be useful to quantify the outcomes of the measures.

Table 2
Hierarchy of options -measures -instruments -SOIs.Source: Authors.Thus, our database [ 19 ] devotes itself to measures, as these are the minimum quantifiable climate action unit, and necessarily includes instruments and indicators for each.From a data point of view, the previous description can be translated into Fig. 2 , which shows how instruments and indicators data are associated with each measure.The methods used to construct this relationship are detailed in the last portion of the Methods section.

Main data types
Two main types of data can be found within the datasheets: (1) continuous (quantitative) and ( 2) nominal (qualitative) ( Table 3 ).We identified reliable sources for both data types in the compiled secondary sources but faced challenges for each.For continuous data, we tried to maintain it in its original form.However, climate action implementation is highly context-dependent [ 2 , 6 , 8 , 9 ].This fact is highlighted by the differences in provided source values.To address this, we assigned value ranges or approximate average values using the raw data.
Nominal data presented a bigger challenge, as no alignment was found between the qualitative attributes within previous sources, which limits transferability and the harmonisation of data.On the contrary, systematic approaches as tools developed by international initiatives are crucial in developing Local Climate Plans [ 10 , 11 ].Local Climate Plans are policy strategies and drafting specific actions to achieve climate goals and are crucial to reduce GHG emissions and adapt to climate change impacts.Therefore, we looked at one of the largest global climate initiatives with more than 11.600 signatories, the Covenant of Mayors, and used their template for developing Sustainable Energy and Climate Action Plans to establish a common framework to categorise the data.In such a manner, data might become transferable from our database to real climate planning practices.

Measures dataset
There are 188 mitigation measures, 191 adaptation measures, and 97 measures that can serve both types of response [ 19 ].We defined 18 different variables according to existing scholarly climate planning literature.The selection of variables defining measures is based on (1) the importance of the gap between planning and implementation, according to the literature, and (2) the required data to provide in the SECAP templates.The 18 variables can be split into four data categories, depending on the information they provide ( Table 4 ).
Measures' descriptors give the basic information of the measure itself, such as their ID number or name ( Table 5 ).
Attributes of the measure are related to the measure's target.After reviewing existing scholarly literature, previously built databases, and climate planning tools like the SECAPs, we concluded that there are four main attributes for measures: type of response (all), sector (SECAPs; Climate-ADAPT; RESCCUE; RESIN; EEA), and -in the case of adaptation -the hazard(s) addressed (RESCCUE; Climate-ADAPT; RESIN; SECAPs), or -in the case of mitigation -, mitigation sectors (SECAPs) ( Table 6 ).
Identifying interactions between measures can increase their effectiveness [ 3 ].As the implementation conditions of measures vary according to their local and regional context [ 3 , 4 , 6 , 10 , 12 ], rather than identifying individual and particular interactions between measures, previous literature showed that identifying the side effects of measures might be the path to follow [ 6 , 13 , 14 ].Thus, we added a second tier of sectors and hazards, indicating the sectors and hazards with which each measure potentially has synergies ( Table 7 ).
The measures dataset [ 19 ] compiles 191 measures addressing only adaptation, 188 addressing mitigation, and 97 measures addressing both.Water is the most addressed sector by measures,  Vulnerable adaptation sectors cover more economic, environmental, and social areas than mitigation sectors.In fact, some sectors serve both mitigation and adaptation.For example, Agriculture and Forestry is also important for mitigation [ 4 ], although not mentioned as mitigation sector in the SECAPs.Likewise, some mitigation measures, e.g.those related to tourism (like promoting local commerce), environment and biodiversity (creating parks and green areas), and education (any course related to shift into behaviour), also relate to adaptation sectors.Therefore, our database adopted the adaptation sectors as the standard measure categorisation.

Affected hazards
The hazards that might be affected in terms of frequency, intensity, or impact when the measure is applied but are not the main target of the measure.It is important to note that some mitigation measures might have synergies with certain hazards, which are not exclusive for adaptation in our database.with 156 measures, followed by energy, with 112.Adaptation measures are typically waterrelated, while mitigation is mostly connected to the transport and energy sector.In fact, the transport mitigation sector is the most addressed by mitigation measures with 62 measures.
Regarding hazards, 84 measures address heavy precipitation, followed by coastal (77) and fluvial (75) flooding measures.In terms of complementary sectors, most measures have an impact on the energy sector (85) and on the environment and biodiversity sector (72).Even if only two measures are specifically addressing the health sector, this one can be impacted by 45 measures.
As for the affected hazards, 31 measures might increase or reduce the effects of extreme heat, and 31 on mass movements, even though only eight measures directly address it ( Figs. 3 and 4 ).Data for implementation refers to the data that might help close the gap between the planning and implementation stages and to develop better-quality plans.An appropriate planning scale [ 3 , 4 ], the proper consideration of implementation times and long-term planning [ 3 , 4 ], and the consideration of justice [ 5 ] increase the feasibility of climate actions.Moreover, identifying responsibility for the action and evaluating costs beforehand, among others, are points to be considered when developing high-quality plans [ 15 ] ( Table 8 ).

Implementation time
It is mandatory for the SECAPs and essential to developing a good quality plan.It is the period between the official publication of a measure into a plan and when the measure starts having an outcome on the targeted KPI.

Measures' Lifetime
An estimated value of a measure's lifetime helps planners account for long-term planning and avoid undesired obsolescence or lack of continuity in the measures.

Origin of the action
Planning the measure at an appropriate scale will help reduce the possible gaps between the planning stage and its implementation.Scale is an essential issue regarding administrative boundaries and taking responsibility for implementation.Thus, this parameter is focused on the administrative scale and is aligned with the SECAPs' "Origin of the action". Nominal: -Local authority (255) -Regional (109) -National (70) -Covenant coordinator or supporter (2) -Mixed ( 6) -Others (-) -NA (206)

Implementation costs
Installation costs refer to the development and implementation costs to make a measure operational.They are given as approximates or ranges using data available at the end of 2022.Double-checking the costs is important due to monetary value fluctuations and inflation rates.Besides, the greater availability of technology is expected to reduce the cost of some measures, and, when possible, the database provides an estimate of cost variation until 2050.Likewise for maintenance costs.

Continuous
( continued on next page ) According to the data collected, most of the measures need to be initiated at a local scale (Local Authority, 225).Meanwhile, looking at stakeholders involved in the implementation, the Business and private sector (318) and Sub-national governments (272) appear the most.As for any cost and time data, not all measures have data available, as it was subject to the availability of secondary data ( Fig. 5 ).Complementary items facilitate coherent, precise, and transferable climate plans.Moreover, they also help to include justice issues and synergies with SDGs from the perspectives measures.On the one hand, including associated instruments allows climate planners to assess mechanisms to define and modify the implementation of a measure and link complementary issues to it, like prioritising vulnerable groups.On the other hand, SOIs allow climate planners to understand how the outcome of a measure can be quantified while always remaining within the framework of the SDGs.

Instruments dataset
While extracting measures from the previously mentioned sources, we detected 609 instruments that can be attached to measures.We assigned eight variables to define the instruments.Four of them are used to describe the instrument itself -name of instrument, instrument ID, description of the instrument, and sources -, while three are used to define its role and match with measures -sector, hazard, and origin of the instrument -, and one classifies them according to existing policy instruments framework -the type of instrument ( Table 9 , Fig. 6 ).

Indicators dataset
Following the findings within the project LOCALISED [ 7 ] we extracted a list of 93 Sustainable Development Goals Oriented Indicators (SOIs).As defining quantifiable goals for climate actions helps improve a climate plan's quality [ 15 ] and identifying synergies with different Sustainable Development Goals might help increase the effectiveness of measures [ 3 ], we linked their work with our measures dataset to assign a set of SOIs to each measure.The links have been established qualitatively, according to the information obtained per indicator and the attributes and information acquired per each measure.In the end, each indicator has also attached data defined by us or extracted from the aforementioned publication ( Table 10 , Fig. 7 ).The method column briefly describes how the indicators can be obtained or calculated, extracted from LOCALISED D5.1 [ 7 ] Nominal

Unit
The unit informs the user on which unit can quantify the indicator, extracted from LOCALISED D5.1 [ 7 ] Nominal

Synergies with SDGs
Synergies with SDGs will indicate from which SDGs are indicators coming and which SDG will be positively affected by a positive outcome of the indicator, extracted from LOCALISED D5.1 [ 7 ] Nominal: From SDG01 to SDG17

Experimental Design, Materials and Methods
The process of building this new database can be summarised into three main steps: (1) compiling climate actions; (2) distinguishing climate actions according to their operational purposes; (3) reframing climate actions according to a standard planning practice framework ( Fig. 8 ).

Compilation of climate actions
The first step of the research was to compile climate actions throughout the European context.To do so, we searched for established climate actions repositories to extract the data, with the following criteria: (1) the databases have an European scope; (2) the databases contain detailed definitions of specific climate actions; (3) the repositories should have been developed based on implemented climate actions; and (4) the information should be field-based.Table 11 summarises shortly the inclusion-exclusion table for the criteria:  Even though all database compile implemented climate actions, not all of them contain detailed information of specific and transferable climate actions.For example, IPCC reports do not contain detailed information, nor are most of their actions specific enough; the OECD Database only focus on the monitoring of the actions, but does not disaggregate them at the detailed level of information; the Climate Policy database contains a broad selection of actions that are not specific nor described including the information we were aiming for.In the end, 10 repositories were used for the database construction.In case some information of the database was missing in the sources, the data was cross-checked with scholarly literature from Scopus and Web of Science and grey literature.

Distinction of climate actions according to the role in the planning process
To reduce climate actions to the same level of abstraction, we developed an original decision tree methodology to objectively discriminate between measures, instruments, and options.Based on the agreed definitions, we designed a decision tree consisting of five-binary questions.According to the consecutive answers to the questions, the method enables climate actions to be systematically designated as a measure, instrument, or option.As the information varies depending on the source and the type of item, the process should be conducted manually.Only measures and instruments are considered for inclusion in the database.
To ensure the method's reliability, the decision tree was tested with five climate planning researchers of different backgrounds and institutions: the University of Twente, the Catalonia Institute for Energy Research, and the Potsdam Institute for Climate Impact Research.The test allowed us to refine the questions and understand the information needed to make the decision tree more robust (i.e., more objective).The subjects received two lists of the same ten randomly selected climate actions.First, they ran the decision tree only relying on the name of the climate action.Then, all available action information was provided and the test was rerun.No contact between the subjects was allowed.We departed from our hypothesis that the more agreement between answers, the more reliable and unbiased the method was.Results showed different levels of agreement according to the available information, but the agreement level increased significantly during the second part of the test.The process can be summarised in the following scheme ( Fig. 9 ).

Harmonising climate actions
As explained, some nominal data needed to be re-categorised into predefined SECAP categories.To conduct this re-categorisation, we executed a semi-automatic keyword-matching method.The keywords were selected after consulting experts from 5 different institutions: University of Twente (UT), Österreichische Gesellschaft für Umwelt und Technik (ÖGUT), Forschungszentrum Jülich (FZJ), Catalonia Institute for Energy Research, and the Potsdam Institute for Climate Impact Research (IREC).Thanks to the different scientific backgrounds, expertise, language skills and cultural origins, a list of maximum 25 keywords was developed per category.The list was then filtered and processed as described in Table 13 , and the final shortlist of keywords can be found in the Table S1 of the supplementary material.Following the matching method, we conducted a review process based on template coding to avoid mismatches and double-counting ( Tables 12 and 14 ).Analysis of the keywords with an anomalous number of hits.
Identification of conflicts and mismatches.
Discard contentless keywords -e.g., "management".A final list of keywords.5 Run a matching algorithm, searching for the final list of keywords in the available raw data.
Categories assigned automatically to each measure, instrument, and indicator.

Table 14
Criteria to identify the role of the category.Source: Author.
The measure explicitly accounts for that, and the attribute is the principal in the definition.
The measure is indirectly related to the category; the attribute acts as a complement to the definition.
The word does not play any role in the definition, and the match was due to word misuse or a different semantic meaning.

Limitations
Limitations on the dataset are related to data availability and the context dependency of measures.First, there are several climate repositories online, and climate action is a trending field which is constantly being updated.The database was built on a selection of climate action repositories following certain criteria, using the most acknowledged and comprehensives ones as starting point.Nevertheless, small scale repositories, practitioners' compilations, on-going studies, and other less well-known databases that contain useful information might have been omitted.
Our data comes from extensive secondary data research.Thus, data on Measures and Basic Implementation are defined by their sources and cannot be modified.Cost and time data provide approximate values and ranges based on the specific sources from particular regions and case studies, while the Origin of the measure and Stakeholders involved may vary according to the implementation process characteristics.Moreover, when no reliable data regarding cost and time was found, then such data was excluded.While the database provides a framework and generic data to identify potential measures and actions, the Basic Implementation data should be checked before using it in practice.Nevertheless, the clear framework allows users and authors to constantly update the database with new values without compromising reliability and relevance over time.

Fig. 1 .
Fig. 1.The role of the different climate action types in the planning and implementation process.Source: Authors.
management plan Collecting high-quality data for flood recovery Updating flood hazard maps Monitoring infrastructures SOIs Number of deaths, missing people, and persons affected by disaster per 10 0.0 0 0 people.Population exposed to flood risk Urban flood risk or areas exposed to flooding Floating roads Instrument Flood management plan Sustainable mobility plans in the cities Include a buffer height for sea level rise when planning new developments SOIs Number of deaths, missing people, and persons affected by disaster per 10 0.0 0 0 people.Traffic accidents with victims (injuries and deaths) per 10 0.0 0 0 passengers Urban flood risk or areas exposed to flooding

Fig. 5 .
Fig. 5. Number of connections between the scale initiating the measures -Origin of the measure -(left) and the stakeholders involved in implementation (right).Source: Authors.
assigned to an indicator.Each indicator has a unique ID.Nominal Description of the indicator Brief description of the indicator, extracted from LOCALISED D5.1 [ 7 ] Nominal Sources References, links, and other information sources used to fill the database, per indicator, extracted from LOCALISED D5.1 [ 7 ] Nominal Sectors The main sectors are related to the highest level of categorisation of the indicators and refer to the field an indicator addresses.The categories are built according to the SECAPs framework (Baseline Emission Inventory and Risks and Vulnerability Assessment) and coincide with the measures' sectors.Nominal:-All (9)-Buildings (19)-Transport (14)-Energy (25)-Water (7)-Waste (1)-Land Use Planning (3)follow the categorisation of hazards in the SECAPs' Risk and Vulnerability Assessments.The different categories coincide with the measures' hazard categorisation.

Fig. 8 .
Fig. 8. Process of construction of the database.Source: Author.

Table 1
Generic description of the database.Source: Author.

Table 3
Types of data contained in the database and how they are displayed.Source: Authors.

Table 4
Variables included in the measures' dataset.Source: Authors.
a Variable transferable to SECAPs.b Estimated values.

Table 6
Attributes of the measures.Definitions, categories and (quantity).Source: Author.Main sectorsThe main sectors are related to the highest level of categorisation of the measures and refer to the field a measure addresses.The categories are built according to the SECAPs framework.There are two main sector types: mitigation sectors and vulnerable adaptation sectors.A measure can address more than one sector.

Table 7
Attributes of measures.Complementary sectors and affected hazards definitions.Source: Author.

Table 8
Data for implementation.Definitions.Source: Author.
NameDescription Data type, categories and (quantity)

Table 11
Inclusion and exclusion table of the criteria used to select the source repositories.Source: Authors.

Table 12
Sources, types, and issues addressed by the 18 variables of the measures.Source: Author.

Table 13
Semi-automatic keyword matching process.Source: Author.